While such works have used different measures of biometric information, what they have in common is the calculation of entropy, which relates to the inter-class variance that can be artificially increased by including a larger number of features. In order to be independent of the number of extracted features and thus to more accurately estimate the intrinsic information, we also need to consider intra-class variance. Both of these variances are encompassed in our proposed biometric uniqueness score based on the Kullback-Leibler divergence [1]}, also denoted as relative entropy, between genuine and impostor distributions. By its definition below, KL-divergence weighs resistance to both false acceptance and false rejection.
